Dataset Information

Identification of a Multi-Long Noncoding RNA Signature for the Diagnosis of Type 1 Diabetes Mellitus.

ABSTRACT: Due to the increasing prevalence of type 1 diabetes mellitus (T1DM) and its complications, there is an urgent need to identify novel methods for predicting the occurrence and understanding the pathogenetic mechanisms of the disease. Accumulated data have demonstrated the potential of long noncoding RNAs (lncRNAs), as biomarkers in establishing diagnosis and predicting prognosis of numerous diseases. Yet, little is known about the expression patterns and regulatory roles of lncRNAs in the pathogenesis of T1DM and whether they can be used as diagnostic biomarkers for the disease. To further explore these questions, in the present study, we conducted a comparative analysis of the expression patterns of lncRNAs between 20 T1DM patients and 42 health controls by retrospectively analyzing a published microarray data set. Our results indicate that, compared with healthy controls, diabetic patients had altered levels of lncRNAs. Then, we used three time cross-validation strategy and support vector machine to propose a specific 26-lncRNA signature (termed 26LncSigT1DM). This 26LncSigT1DM signature can be used to effectively distinguish between healthy and diabetic individuals (area under the curve = 0.825) of a validation cohort. After the 26LncSigT1DM was prospectively validated, we used Pearson correlation to identify 915 mRNAs, whose expression levels were positively correlated with those of the 26 lncRNAs. According to their Gene Ontology annotations, these mRNAs participate in processes including cellular response to stimulus, cell communication, multicellular organismal process, and cell motility. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that the genes encoding the 915 mRNAs may be associated with the NOD-like receptor signaling pathway, transforming growth factor ? signaling pathway, and mineral absorption, suggesting that the deregulation of these lncRNAs may mediate inflammatory abnormalities and immune dysfunctions, which jointly promote the pathogenesis of T1DM. Thus, our study identifies a novel diagnostic tool and may shed more light on the molecular mechanisms underlying the pathogenesis of T1DM.

SUBMITTER: Geng G

PROVIDER: S-EPMC7350420 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of a Multi-Long Noncoding RNA Signature for the Diagnosis of Type 1 Diabetes Mellitus.

Geng Guannan G Zhang Zicheng Z Cheng Liang L

Frontiers in bioengineering and biotechnology 20200703

Due to the increasing prevalence of type 1 diabetes mellitus (T1DM) and its complications, there is an urgent need to identify novel methods for predicting the occurrence and understanding the pathogenetic mechanisms of the disease. Accumulated data have demonstrated the potential of long noncoding RNAs (lncRNAs), as biomarkers in establishing diagnosis and predicting prognosis of numerous diseases. Yet, little is known about the expression patterns and regulatory roles of lncRNAs in the pathoge ...[more]

PMID: 32719778

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Project description:Objective- Macrophages play key roles in inflammation and diabetic vascular complications. Emerging evidence implicates long noncoding RNAs in inflammation, but their role in macrophage dysfunction associated with inflammatory diabetic complications is unclear and was therefore investigated in this study. Approach and Results- RNA-sequencing and real-time quantitative PCR demonstrated that a long noncoding RNA Dnm3os (dynamin 3 opposite strand) is upregulated in bone marrow-derived macrophages from type 2 diabetic db/db mice, diet-induced insulin-resistant mice, and diabetic ApoE-/- mice, as well as in monocytes from type 2 diabetic patients relative to controls. Diabetic conditions (high glucose and palmitic acid) induced Dnm3os in mouse and human macrophages. Promoter reporter analysis and chromatin immunoprecipitation assays demonstrated that diabetic conditions induce Dnm3os via NF-κB activation. RNA fluorescence in situ hybridization and real-time quantitative PCRs of subcellular fractions demonstrated nuclear localization and chromatin enrichment of Dnm3os in macrophages. Stable overexpression of Dnm3os in macrophages altered global histone modifications and upregulated inflammation and immune response genes and phagocytosis. Conversely, RNAi-mediated knockdown of Dnm3os attenuated these responses. RNA pull-down assays with macrophage nuclear lysates identified nucleolin and ILF-2 (interleukin enhancer-binding factor 2) as protein binding partners of Dnm3os, which was further confirmed by RNA fluorescence in situ hybridization immunofluorescence. Furthermore, nucleolin levels were decreased in diabetic conditions, and its knockdown enhanced Dnm3os-induced inflammatory gene expression and histone H3K9-acetylation at their promoters. Conclusions- These results demonstrate novel mechanisms involving upregulation of long noncoding RNA Dnm3os, disruption of its interaction with nucleolin, and epigenetic modifications at target genes that promote macrophage inflammatory phenotype in diabetes mellitus. The data could lead to long noncoding RNA-based therapies for inflammatory diabetes mellitus complications.

Project description:BackgroundNumerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided.MethodsThis study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance.ResultsThe results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

Project description:BackgroundDiabetic retinopathy (DR) is a leading cause of blindness in the working-age population, which is characterized by retinal neurodegeneration and vascular dysfunction. Long non-coding RNAs (LncRNAs) have emerged as critical regulators in several biological processes and disease progression. Here we investigated the role of lncRNA AQP4-AS1 in retinal neurovascular dysfunction induced by diabetes.MethodsQuantitative RT-PCR was used to detect the AQP4-AS1 expression pattern upon diabetes mellitus-related stresses. Visual electrophysiology examination, TUNEL staining, Evans blue staining, retinal trypsin digestion and immunofluorescent staining were conducted to detect the role of AQP4-AS1 in retinal neurovascular dysfunction in vivo. MTT assays, TUNEL staining, PI/Calcein-AM staining, EdU incorporation assay transwell assay and tube formation were conducted to detect the role of AQP4-AS1 in retinal cells function in vitro. qRT-PCR, western blot and in vivo studies were conducted to reveal the mechanism of AQP4-AS1-mediated retinal neurovascular dysfunction.FindingsAQP4-AS1 was significantly increased in the clinical samples of diabetic retinopathy patients, high glucose-treated Müller cells, and diabetic retinas of a murine model. AQP4-AS1 silencing in vivo alleviated retinal neurodegeneration and vascular dysfunction as shown by improved retinal capillary degeneration, decreased reactive gliosis, and reduced RGC loss. AQP4-AS1 directly regulated Müller cell function and indirectly affected endothelial cell and RGC function in vitro. Mechanistically, AQP4-AS1 regulated retinal neurovascular dysfunction through affecting AQP4 levels.InterpretationThis study reveals AQP4-AS1 is involved in retinal neurovascular dysfunction and expected to become a promising target for the treatment of neurovascular dysfunction in DR.FundingThis work was generously supported by the grants from the National Natural Science Foundation of China (Grant No. 81800858, 82070983, 81870679 and 81970823), grants from the Medical Science and Technology Development Project Fund of Nanjing (Grant No ZKX17053 and YKK19158), grants from Innovation Team Project Fund of Jiangsu Province (No. CXTDB2017010), and the Science and Technology Development Plan Project Fund of Nanjing (Grant No 201716007, 201805007 and 201803058).

Dataset Information

Identification of a Multi-Long Noncoding RNA Signature for the Diagnosis of Type 1 Diabetes Mellitus.

Publications

Identification of a Multi-Long Noncoding RNA Signature for the Diagnosis of Type 1 Diabetes Mellitus.

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